code: def test_noisy_parabola():
      def f(x, sd=1):
          y = (x - 3) ** 2 + 10.0
          if sd == 0:
              return y
          else:
              return y + numpy.random.normal(
                  loc=0.0, scale=sd, size=numpy.array(x).shape
              )
  
      parameters = [sherpa.Continuous("x1", [0.0, 7.0])]
  
      bayesian_optimization = GPyOpt(
          max_concurrent=1,
          max_num_trials=5,
          model_type="GP",
          acquisition_type="EI",
      )
      rep = Repeat(
          algorithm=bayesian_optimization, num_times=3, agg=True
      )
      study = sherpa.Study(
          algorithm=rep,
          parameters=parameters,
          lower_is_better=True,
          disable_dashboard=True,
      )
  
      for trial in study:
          # print("Trial {}:\t{}".format(trial.id, trial.parameters))
  
          fval = f(trial.parameters["x1"], sd=1)
          # print("Function Value: {}".format(fval))
          study.add_observation(
              trial=trial, iteration=1, objective=fval
          )
          study.finalize(trial, status="COMPLETED")
      # rval = study.get_best_result()
      # print(rval)
      print(study.results.query("Status=='COMPLETED'"))
  
imports: import sherpa
  import pandas
  import pytest
  import numpy
  import collections
  import GPyOpt
  from sherpa.algorithms.bayesian_optimization import GPyOpt
  from sherpa.algorithms import Repeat
label_desc: Use image node_id 21 for calling a global function with example usage: test_noisy_parabola() and returns: y, unknown